Filtering noisy/high-intensity regions in laser-based lane marker detection

ABSTRACT

An autonomous vehicle may be configured to receive, using a computer system, a plurality of remission signals from a portion of a lane of travel in an environment in response to at least one sensor of the vehicle sensing the portion of the lane of travel. A given remission signal of the plurality of remission signals may include a remission value indicative of a level of reflectiveness for the portion of the lane of travel. The vehicle may also be configured to compare the plurality of remission signals to a known remission value indicative of a level of reflectiveness for a lane marker in the lane of travel. Based on the comparison, the vehicle may additionally be configured to determine whether the portion of the lane of travel in the environment is indicative of a presence of the lane marker.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 13/956,871, filed on Aug. 1, 2013, and entitled “FilteringNoisy/High-Intensity Regions in Laser-Based Lane Marker Detection,”which is herein incorporated by reference as if fully set forth in thisdescription.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Vehicles can be configured to operate in an autonomous mode in which thevehicle navigates through an environment with little or no input from adriver. Such autonomous vehicles can include one or more sensors thatare configured to detect information about the environment in which thevehicle operates. The vehicle and its associated computer-implementedcontroller use the detected information to navigate through theenvironment. For example, if the sensor(s) detect that the vehicle isapproaching an obstacle, as determined by the computer-implementedcontroller, the controller adjusts the directional controls of thevehicle to cause the vehicle to navigate around the obstacle.

SUMMARY

Within examples, methods and systems are provided for filtering noisy orhigh-intensity regions in laser-based lane marker detection.

In a first aspect, a method is provided. The method may includereceiving, using a processor, a plurality of remission signals based ona portion of a lane of travel in an environment in response to at leastone sensor of a vehicle sensing the portion of the lane of travel. Agiven remission signal of the plurality of remission signals may includea remission value indicative of a level of reflectiveness for theportion of the lane of travel. The vehicle may be configured to operatein an autonomous mode in the environment and may be substantially in thelane of travel in the environment. The method may also include comparingthe plurality of remission signals to a known remission value indicativeof a level of reflectiveness for a lane marker in the lane of travel.The method may additionally include based on the comparison, determiningwhether the portion of the lane of travel in the environment isindicative of a presence of the lane marker.

In a second aspect, a vehicle is provided. The vehicle may include asensor and a computer system. The sensor may be configured to sense alane of travel in an environment of the vehicle. The vehicle may beconfigured to operate in an autonomous mode in the environment, and thevehicle may be substantially in the lane of travel in the environment.The computer system may be configured to receive a plurality ofremission signals based on a portion of the lane of travel in theenvironment in response to the sensor sensing the portion of the lane oftravel. The computer system may also be configured to compare theplurality of remission signals to a known remission value indicative ofa level of reflectiveness for a lane marker in the lane of travel. Thecomputer system may additionally be configured to, based on thecomparison, determine whether the portion of the lane of travel in theenvironment is indicative of a presence of the lane marker.

In a third aspect, a non-transitory computer readable medium havingstored therein instructions that when executed by a computer system in avehicle, cause the computer system to perform functions is disclosed.The functions may include receiving a plurality of remission signalsbased on a portion of a lane of travel in an environment in response toat least one sensor of a vehicle sensing the portion of the lane oftravel. A given remission signal of the plurality of remission signalsmay include a remission value indicative of a level of reflectivenessfor the portion of the lane of travel. The vehicle may be configured tooperate in an autonomous mode in the environment and may besubstantially in the lane of travel in the environment. The functionsmay also include comparing the plurality of remission signals to a knownremission value indicative of a level of reflectiveness for a lanemarker in the lane of travel. The functions may additionally include,based on the comparison, determining whether the portion of the lane oftravel in the environment is indicative of a presence of the lanemarker.

In a fourth aspect a system is disclosed. The system may include a meansfor receiving a plurality of remission signals based on a portion of alane of travel in an environment in response to at least one sensor ofthe vehicle sensing the portion of the lane of travel. A given remissionsignal of the plurality of remission signals may include a remissionvalue indicative of a level of reflectiveness for the portion of thelane of travel. The vehicle may be configured to operate in anautonomous mode in the environment and may be substantially in the laneof travel in the environment. The system may also include a means forcomparing the plurality of remission signals to a known remission valueindicative of a level of reflectiveness for a lane marker in the lane oftravel. The system may additionally include a means for based on thecomparison, determining whether the portion of the lane of travel in theenvironment is indicative of a presence of the lane marker.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the figures and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a functional block diagram illustrating a vehicle, inaccordance with an example embodiment.

FIG. 2 is a vehicle, in accordance with an example embodiment.

FIG. 3A illustrates a block diagram of a method, in accordance with anexample embodiment.

FIG. 3B illustrates another block diagram of a method, in accordancewith an example embodiment.

FIG. 4 is a conceptual side view illustration of filtering noisy orhigh-intensity regions in laser-based lane marker detection, inaccordance with an example embodiment.

FIG. 5 is another conceptual side view illustration of filtering noisyor high-intensity regions in laser-based lane marker detection, inaccordance with an example embodiment.

FIG. 6 is a schematic diagram of a computer program product, accordingto an example embodiment.

DETAILED DESCRIPTION

Example methods and systems are described herein. Any example embodimentor feature described herein is not necessarily to be construed aspreferred or advantageous over other embodiments or features. Theexample embodiments described herein are not meant to be limiting. Itwill be readily understood that certain aspects of the disclosed systemsand methods can be arranged and combined in a wide variety of differentconfigurations, all of which are contemplated herein.

Furthermore, the particular arrangements shown in the Figures should notbe viewed as limiting. It should be understood that other embodimentsmay include more or less of each element shown in a given Figure.Further, some of the illustrated elements may be combined or omitted.Yet further, an example embodiment may include elements that are notillustrated in the Figures.

For vehicles operating in an autonomous mode, sensing and detecting themarkers that specify the boundaries of different traffic lanes may beimportant. Generally, to detect the markers and traffic lanes, anautonomous vehicle may utilize a laser imaging detection and radar(LIDAR) to periodically scan the lane in which the vehicle is operatingand provide discriminating signals based on increased reflectance of thelane marker. For example, the vehicle may determine the presence of thelane markers because the lane markers may have higher remission signalsthan the remission signals produced by other portions of the lane. Basedon the signals, the autonomous vehicle may differentiate between thelane and the lane markers and thereby determine the boundaries of thelane. However, LIDAR remission signals may generally be susceptible tosignal noise, and therefore it may be difficult to separate the desiredsignal from the noise. Additionally, some regions in the lane may havehigher remission signals than usual, which may make the lane remissionsignals indistinguishable to the lane marker remission signals.

Within examples, an autonomous vehicle is provided that is configured tofilter out noise that may be associated with LIDAR remission signalsgenerated when sensing a lane of travel, and filter out regions of thelane of travel that may generally have high remission signals. Disclosedherein are methods and systems that may allow an autonomous vehicle toseparate signal noise from valid remission signals and to determine andrecognize regions of a lane of travel that may produce high remissionsignals, but do not represent lane markers. Disclosed embodiments relateto an autonomous vehicle that may be configured to use a LIDAR to sensea particular lane of travel in an environment in which the vehicle isoperating. Using the LIDAR to sense a portion of the lane, the vehiclemay receive remission signals that may include remission valuesindicative of an amount of light that is received by the LIDAR when theLIDAR senses the portion of the lane. The vehicle may compare the valuesobtained from the remission signals to a known remission value thatgenerally represents a level of reflectiveness for a lane marker. Basedon the comparison, the vehicle may determine whether the portion of thelane of travel corresponds to the lane marker. Based on thedetermination, the vehicle may operate accordingly in the lane of travel(e.g., shift left or right to remain within the bounds of the lanemarker).

Utilizing the methods and systems described herein may help anautonomous vehicle to more accurately use a LIDAR to sense and detectlane markers in a lane of travel. The methods and systems describedherein may also be more widely acceptable to novel environments and maybe used to address changing road conditions in a more robust manner.

Example systems will now be described in greater detail. Generally, anexample system may be implemented in or may take the form of a computingdevice. However, an example system may also be implemented in or takethe form of other devices or may be included within vehicles, such ascars, trucks, motorcycles, buses, boats, airplanes, helicopters, lawnmowers, recreational vehicles, amusement park vehicles, farm equipment,construction equipment, trams, golf carts, trains, and trolleys. Othervehicles are possible as well.

FIG. 1 is a functional block diagram depicting a vehicle 100 accordingto an example embodiment. The vehicle 100 is configured to operate fullyor partially in an autonomous mode, and thus may be referred to as an“autonomous vehicle.” For example, a computer system 112 may control thevehicle 100 while in an autonomous mode via control instructions to acontrol system 106 for the vehicle 100. The computer system 112 mayreceive information from a sensor system 104, and base one or morecontrol processes (such as the setting a heading so as to avoid adetected obstacle) upon the received information in an automatedfashion.

The vehicle 100 may be fully autonomous or partially autonomous. In apartially autonomous vehicle some functions can optionally be manuallycontrolled (e.g., by a driver) some or all of the time. Further, apartially autonomous vehicle may be configured to switch between afully-manual operation mode and a partially-autonomous and/or afully-autonomous operation mode.

The vehicle 100 may include various subsystems such as a propulsionsystem 102, a sensor system 104, a control system 106, one or moreperipherals 108, as well as a power supply 110, a computer system 112,and a user interface 116. The vehicle 100 may include more or fewersubsystems and each subsystem may include multiple elements. Further,each of the subsystems and elements of vehicle 100 may beinterconnected. Thus, one or more of the described functions of thevehicle 100 may be divided up into additional functional or physicalcomponents, or combined into fewer functional or physical components. Insome further examples, additional functional and/or physical componentsmay be added to the examples illustrated by FIG. 1.

The propulsion system 102 may include components operable to providepowered motion to the vehicle 100. Depending upon the embodiment, thepropulsion system 102 may include an engine/motor 118, an energy source119, a transmission 120, and wheels/tires 121. The engine/motor 118could be any combination of an internal combustion engine, an electricmotor, steam engine, Stirling engine, or other types of engines and/ormotors. In some embodiments, the propulsion system 102 may includemultiple types of engines and/or motors. For instance, a gas-electrichybrid vehicle may include a gasoline engine and an electric motor.Other examples are possible as well.

The energy source 119 may represent a source of energy that may, in fullor in part, power the engine/motor 118. That is, the engine/motor 118may be configured to convert the energy source 119 into mechanicalenergy to operate the transmission 120. Examples of energy sources 119may include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, solar panels, batteries,capacitors, flywheels, regenerative braking systems, and/or othersources of electrical power, etc. The energy source 119 may also provideenergy for other systems of the automobile 100.

The transmission 120 may include elements that are operable to transmitmechanical power from the engine/motor 118 to the wheels/tires 121. Suchelements may include a gearbox, a clutch, a differential, a drive shaft,and/or axle(s), etc. The transmission 120 may include other elements aswell. The drive shafts may include one or more axles that may be coupledto the one or more wheels/tires 121.

The wheels/tires 121 may be arranged to stably support the vehicle 100while providing frictional traction with a surface, such as a road, uponwhich the vehicle 100 moves. Accordingly, the wheels/tires 121 ofvehicle 100 may be configured in various formats, including a unicycle,bicycle/motorcycle, tricycle, or car/truck four-wheel format. Otherwheel/tire geometries are possible, such as those including six or morewheels. Any combination of the wheels/tires 121 of vehicle 100 may beoperable to rotate differentially with respect to other wheels/tires121. The wheels/tires 121 may represent at least one wheel that isfixedly attached to the transmission 120 and at least one tire coupledto a rim of the wheel that could make contact with the driving surface.The wheels/tires 121 may include any combination of metal and rubber, oranother combination of materials.

The sensor system 104 generally includes one or more sensors configuredto detect information about the environment surrounding the vehicle 100.For example, the sensor system 104 may include a Global PositioningSystem (GPS) 122, an inertial measurement unit (IMU) 124, a RADAR unit126, a laser rangefinder/LIDAR unit 128, a camera 130, and/or amicrophone 131. The sensor system 104 may also include sensorsconfigured to monitor internal systems of the vehicle 100 (e.g., O₂monitor, fuel gauge, engine oil temperature, wheel speed sensors, etc.).One or more of the sensors included in the sensor system 104 may beconfigured to be actuated separately and/or collectively in order tomodify a position and/or an orientation of the one or more sensors.

The GPS 122 may be any sensor configured to estimate a geographiclocation of the vehicle 100. To this end, GPS 122 may include atransceiver operable to provide information regarding the position ofthe vehicle 100 with respect to the Earth.

The IMU 124 may include any combination of sensors (e.g., accelerometersand gyroscopes) configured to sense position and orientation changes ofthe vehicle 100 based on inertial acceleration.

The RADAR unit 126 may represent a system that utilizes radio signals tosense objects within the local environment of the vehicle 100. In someembodiments, in addition to sensing the objects, the RADAR unit 126 mayadditionally be configured to sense the speed and/or heading of theobjects.

Similarly, the laser rangefinder or LIDAR unit 128 may be any sensorconfigured to sense objects in the environment in which the vehicle 100is located using lasers. Depending upon the embodiment, the laserrangefinder/LIDAR unit 128 could include one or more laser sources, alaser scanner, and one or more detectors, among other system components.The laser rangefinder/LIDAR unit 128 could be configured to operate in acoherent (e.g., using heterodyne detection) or an incoherent detectionmode.

The camera 130 may include one or more devices configured to capture aplurality of images of the environment surrounding the vehicle 100. Thecamera 130 may be a still camera or a video camera. In some embodiments,the camera 130 may be mechanically movable such as by rotating and/ortilting a platform to which the camera is mounted. As such, a controlprocess of the vehicle 100 may be implemented to control the movement ofthe camera 130.

The sensor system 104 may also include a microphone 131. The microphone131 may be configured to capture sound from the environment surroundingthe vehicle 100. In some cases, multiple microphones can be arranged asa microphone array, or possibly as multiple microphone arrays.

The control system 106 may be configured to control operation(s) of thevehicle 100 and its components. Accordingly, the control system 106 mayinclude various elements include steering unit 132, throttle 134, brakeunit 136, a sensor fusion algorithm 138, a computer vision system 140, anavigation/pathing system 142, and an obstacle avoidance system 144,etc.

The steering unit 132 may represent any combination of mechanisms thatmay be operable to adjust the heading of vehicle 100. For example, thesteering unit 132 can adjust the axis (or axes) of one or more of thewheels/tires 121 so as to effect turning of the vehicle 100. Thethrottle 134 may be configured to control, for instance, the operatingspeed of the engine/motor 118 and, in turn, control the speed of thevehicle 100. The brake unit 136 may include any combination ofmechanisms configured to decelerate the vehicle 100. The brake unit 136may, for example, use friction to slow the wheels/tires 121. In otherembodiments, the brake unit 136 inductively decelerates the wheels/tires121 by a regenerative braking process to convert kinetic energy of thewheels/tires 121 to electric current. The brake unit 136 may take otherforms as well.

The sensor fusion algorithm 138 may be an algorithm (or a computerprogram product storing an algorithm) configured to accept data from thesensor system 104 as an input. The data may include, for example, datarepresenting information sensed at the sensors of the sensor system 104.The sensor fusion algorithm 138 may include, for instance, a Kalmanfilter, Bayesian network, or other algorithm. The sensor fusionalgorithm 138 may provide various assessments based on the data fromsensor system 104. Depending upon the embodiment, the assessments mayinclude evaluations of individual objects and/or features in theenvironment of vehicle 100, evaluations of particular situations, and/orevaluations of possible impacts based on the particular situation. Otherassessments are possible.

The computer vision system 140 may be any system operable to process andanalyze images captured by camera 130 in order to identify objectsand/or features in the environment of vehicle 100 that could includetraffic signals, road way boundaries, other vehicles, pedestrians,and/or obstacles, etc. The computer vision system 140 may use an objectrecognition algorithm, a Structure From Motion (SFM) algorithm, videotracking, and other computer vision techniques. In some embodiments, thecomputer vision system 140 could be additionally configured to map anenvironment, track objects, estimate the speed of objects, etc.

The navigation and pathing system 142 may be any system configured todetermine a driving path for the vehicle 100. For example, thenavigation/pathing system 142 may determine a series of speeds anddirectional headings to effect movement of the vehicle 100 along a paththat substantially avoids perceived obstacles while generally advancingthe vehicle 100 along a roadway-based path leading to an ultimatedestination, which may be set according to user inputs via the userinterface 116, for example. The navigation and pathing system 142 mayadditionally be configured to update the driving path dynamically whilethe vehicle 100 is in operation. In some embodiments, the navigation andpathing system 142 could be configured to incorporate data from thesensor fusion algorithm 138, the GPS 122, and one or more predeterminedmaps so as to determine the driving path for vehicle 100.

The obstacle avoidance system 144 may represent a control systemconfigured to identify, evaluate, and avoid or otherwise negotiatepotential obstacles in the environment of the vehicle 100. For example,the obstacle avoidance system 144 may effect changes in the navigationof the vehicle 100 by operating one or more subsystems in the controlsystem 106 to undertake swerving maneuvers, turning maneuvers, brakingmaneuvers, etc. In some embodiments, the obstacle avoidance system 144is configured to automatically determine feasible (“available”) obstacleavoidance maneuvers on the basis of surrounding traffic patterns, roadconditions, etc. For example, the obstacle avoidance system 144 may beconfigured such that a swerving maneuver is not undertaken when othersensor systems detect vehicles, construction barriers, other obstacles,etc. in the region adjacent the vehicle 100 that would be swerved into.In some embodiments, the obstacle avoidance system 144 may automaticallyselect the maneuver that is both available and maximizes safety ofoccupants of the vehicle. For example, the obstacle avoidance system 144may select an avoidance maneuver predicted to cause the least amount ofacceleration in a passenger cabin of the vehicle 100.

The control system 106 may additionally or alternatively includecomponents other than those shown and described.

The vehicle 100 also includes peripherals 108 configured to allowinteraction between the vehicle 100 and external sensors, othervehicles, other computer systems, and/or a user, such as an occupant ofthe vehicle 100. For example, the peripherals 108 for receivinginformation from occupants, external systems, etc. may include awireless communication system 146, a touchscreen 148, a microphone 150,and/or a speaker 152.

In some embodiments, the peripherals 108 function to receive inputs fora user of the vehicle 100 to interact with the user interface 116. Tothis end, the touchscreen 148 can both provide information to a user ofthe vehicle 100, and convey information from the user indicated via thetouchscreen 148 to the user interface 116. The touchscreen 148 can beconfigured to sense both touch positions and touch gestures from thefinger of a user (or stylus, etc.) via capacitive sensing, resistancesensing, optical sensing, a surface acoustic wave process, etc. Thetouchscreen 148 can be capable of sensing finger movement in a directionparallel or planar to the touchscreen surface, in a direction normal tothe touchscreen surface, or both, and may also be capable of sensing alevel of pressure applied to the touchscreen surface. An occupant of thevehicle 100 can also utilize a voice command interface. For example, themicrophone 150 can be configured to receive audio (e.g., a voice commandor other audio input) from an occupant of the vehicle 100. Similarly,the speaker 152 can be configured to output audio to the occupant of thevehicle 100.

In some embodiments, the peripherals 108 function to allow communicationbetween the vehicle 100 and external systems, such as devices, sensors,other vehicles, etc. within its surrounding environment and/orcontrollers, servers, etc., physically located far from the vehicle 100that provide useful information regarding the vehicle's surroundings,such as traffic information, weather information, etc. For example, thewireless communication system 146 can wirelessly communicate with one ormore devices directly or via a communication network. The wirelesscommunication system 146 can optionally use 3G cellular communication,such as CDMA, EVDO, GSM/GPRS, and/or 4G cellular communication, such asWiMAX or LTE. Additionally or alternatively, the wireless communicationsystem 146 can communicate with a wireless local area network (WLAN),for example, using WiFi. In some embodiments, the wireless communicationsystem 146 could communicate directly with a device, for example, usingan infrared link, Bluetooth, and/or ZigBee. The wireless communicationsystem 146 can include one or more dedicated short range communication(DSRC) devices that can include public and/or private datacommunications between vehicles and/or roadside stations. Other wirelessprotocols for sending and receiving information embedded in signals,such as various vehicular communication systems, can also be employed bythe wireless communication system 146 within the context of the presentdisclosure.

The power supply 110 may provide power to components of the vehicle 100,such as electronics in the peripherals 108, the computer system 112, thesensor system 104, etc. The power supply 110 can include a rechargeablelithium-ion or lead-acid battery for storing and discharging electricalenergy to the various powered components, for example. In someembodiments, one or more banks of batteries may be configured to provideelectrical power. In some embodiments, the power supply 110 and theenergy source 119 can be implemented together, as in some all-electriccars.

Many or all of the functions of the vehicle 100 may be controlled viathe computer system 112 that receives inputs from the sensor system 104,the peripherals 108, etc., and communicates appropriate control signalsto the propulsion system 102, the control system 106, the peripherals108, etc. to effect automatic operation of the vehicle 100 based on itssurroundings. The computer system 112 may include at least one processor113 (which could include at least one microprocessor) that executesinstructions 115 stored in a non-transitory computer readable medium,such as the data storage 114. The computer system 112 may also representa plurality of computing devices that may serve to control individualcomponents or subsystems of the vehicle 100 in a distributed fashion.

In some embodiments, data storage 114 may contain instructions 115(e.g., program logic) executable by the processor 113 to execute variousautomobile functions, including those described above in connection withFIG. 1. Data storage 114 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, and/or control one or more of the propulsion system 102, thesensor system 104, the control system 106, and the peripherals 108.

In addition to the instructions 115, the data storage 114 may store datasuch as roadway maps, path information, among other information. Suchinformation may be used by vehicle 100 and computer system 112 at duringthe operation of the vehicle 100 in the autonomous, semi-autonomous,and/or manual modes.

The vehicle 100, and associated computer system 112, providesinformation to and/or receives input from, a user of the vehicle 100,such as an occupant in a passenger cabin of the vehicle 100.Accordingly, the vehicle 100 may include a user interface 116 forproviding information to or receiving input from a user of vehicle 100.The user interface 116 may control or enable control of content and/orthe layout of interactive images that could be displayed on thetouchscreen 148. Further, the user interface 116 could include one ormore input/output devices within the set of peripherals 108, such as thewireless communication system 146, the touchscreen 148, the microphone150, and the speaker 152.

The computer system 112 controls the operation of the vehicle 100 basedon inputs received from various subsystems indicating vehicle and/orenvironmental conditions (e.g., propulsion system 102, sensor system104, and/or control system 106), as well as inputs from the userinterface 116, indicating user preferences. For example, the computersystem 112 may utilize input from the control system 106 to control thesteering unit 132 to avoid an obstacle detected by the sensor system 104and the obstacle avoidance system 144. The computer system 112 may beconfigured to control many aspects of the vehicle 100 and itssubsystems. Generally, however, provisions are made for manuallyoverriding automated controller-driven operation, such as in the eventof an emergency, or merely in response to a user-activated override,etc.

The components of the vehicle 100 described herein may be configured towork in an interconnected fashion with other components within oroutside their respective systems. For example, the camera 130 cancapture a plurality of images that represent information about anenvironment of the vehicle 100 while operating in an autonomous mode.The environment may include other vehicles, traffic lights, trafficsigns, road markers, pedestrians, etc. The computer vision system 140can categorize and/or recognize various aspects in the environment inconcert with the sensor fusion algorithm 138, the computer system 112,etc. based on object recognition models pre-stored in the data storage114, and/or by other techniques.

Although FIG. 1 shows various components of vehicle 100, i.e., wirelesscommunication system 146, computer system 112, data storage 114, anduser interface 116, as being integrated into the vehicle 100, one ormore of these components could be mounted or associated separately fromthe vehicle 100. For example, data storage 114 could, in part or infull, exist separate from the vehicle 100. Thus, the vehicle 100 couldbe provided in the form of device elements that may be locatedseparately or together. The device elements that make up vehicle 100 maygenerally be communicatively coupled together in a wired and/or wirelessfashion.

FIG. 2 depicts an example vehicle 200 that can include all or most ofthe functions described in connection with the vehicle 100 in referenceto FIG. 1. Although example vehicle 200 is illustrated in FIG. 2 as afour-wheel sedan-type car for illustrative purposes, the presentdisclosure is not so limited. For instance, example vehicle 200 canrepresent any type of vehicle mentioned herein.

Example vehicle 200 includes a sensor unit 202, a wireless communicationsystem 204, a LIDAR unit 206, a laser rangefinder unit 208, and a camera210. Furthermore, example vehicle 200 may include any of the componentsdescribed in connection with vehicle 100 of FIG. 1.

The sensor unit 202 is mounted atop example vehicle 200 and includes oneor more sensors configured to detect information about an environmentsurrounding example vehicle 200, and output indications of theinformation. For example, the sensor unit 202 may include anycombination of cameras, RADARs, LIDARs, range finders, and acousticsensors. The sensor unit 202 may include one or more movable mounts thatmay be operable to adjust the orientation of one or more sensors in thesensor unit 202. In one embodiment, the movable mount may include arotating platform that may scan sensors so as to obtain information fromeach direction around example vehicle 200. In another embodiment, themovable mount of the sensor unit 202 may be moveable in a scanningfashion within a particular range of angles and/or azimuths. The sensorunit 202 may be mounted atop the roof of a car, for instance, howeverother mounting locations are possible. Additionally, the sensors of thesensor unit 202 may be distributed in different locations and need notbe collocated in a single location. Some possible sensor types andmounting locations include the LIDAR unit 206 and laser rangefinder unit208. Furthermore, each sensor of the sensor unit 202 may be configuredto be moved or scanned independently of other sensors of the sensor unit202.

The wireless communication system 204 may be located on a roof ofexample vehicle 200 as depicted in FIG. 2. Alternatively, the wirelesscommunication system 204 may be located, fully or in part, elsewhere.The wireless communication system 204 may include wireless transmittersand receivers that may be configured to communicate with devicesexternal or internal to example vehicle 200. Specifically, the wirelesscommunication system 204 may include transceivers configured tocommunicate with other vehicles and/or computing devices, for instance,in a vehicular communication system or a roadway station. Examples ofsuch vehicular communication systems include dedicated short rangecommunications (DSRC), radio frequency identification (RFID), and otherproposed communication standards directed towards intelligent transportsystems.

The camera 210 may be a photo-sensitive instrument, such as a stillcamera, a video camera, etc., that is configured to capture a pluralityof images of the environment of example vehicle 200. To this end, thecamera 210 can be configured to detect visible light, and canadditionally or alternatively be configured to detect light from otherportions of the spectrum, such as infrared or ultraviolet light. Thecamera 210 can be a two-dimensional detector, and can optionally have athree-dimensional spatial range of sensitivity. In some embodiments, thecamera 210 can include, for example, a range detector configured togenerate a two-dimensional image indicating distance from the camera 210to a number of points in the environment. To this end, the camera 210may use one or more range detecting techniques.

For example, the camera 210 may provide range information by using astructured light technique in which example vehicle 200 illuminates anobject in the environment with a predetermined light pattern, such as agrid or checkerboard pattern and uses the camera 210 to detect areflection of the predetermined light pattern from environmentalsurroundings. Based on distortions in the reflected light pattern,example vehicle 200 may determine the distance to the points on theobject. The predetermined light pattern may comprise infrared light, orradiation at other suitable wavelengths for such measurements.

The camera 210 may be mounted inside a front windshield of examplevehicle 200. Specifically, the camera 210 may be situated to captureimages from a forward-looking view with respect to the orientation ofexample vehicle 200. Other mounting locations and viewing angles of thecamera 210 may also be used, either inside or outside example vehicle200.

The camera 210 can have associated optics operable to provide anadjustable field of view. Further, the camera 210 may be mounted toexample vehicle 200 with a movable mount to vary a pointing angle of thecamera 210, such as a via a pan/tilt mechanism.

In FIG. 3A, a method 300 is provided for filtering noisy orhigh-intensity regions in laser-based lane marker detection. The vehicledescribed in this method may be vehicle 100 and/or vehicle 200 asillustrated and described in reference to FIGS. 1 and 2, respectively,or components of the vehicle 100 or vehicle 200. For example, theprocesses described herein may be carried out by RADAR unit 126, LIDARunit 128, and/or camera 130 mounted to an autonomous vehicle (e.g.,vehicle 200) in communication with computer system 112, sensor fusionalgorithm 138, and/or computer vision system 140.

Furthermore, it is noted that the functionality described in connectionwith the flowcharts described herein can be implemented asspecial-function and/or configured general-function hardware modules,portions of program code executed by a processor (e.g., the processor113 in the computer system 112) for achieving specific logicalfunctions, determinations, and/or steps described in connection with theflowchart shown in FIG. 3A (or FIG. 3B). Where used, program code can bestored on any type of computer-readable medium (e.g., computer-readablestorage medium or non-transitory media, such as the data storage 114described above with respect to computer system 112 and/or a computerprogram product 500 described below), for example, such as a storagedevice including a disk or hard drive.

In addition, each block of the flowchart shown in FIG. 3A (or FIG. 3B)may represent circuitry that is wired to perform the specific logicalfunctions in the process. Unless specifically indicated, functions inthe flowchart shown in FIGS. 3A-3B may be executed out of order fromthat shown or discussed, including substantially concurrent execution ofseparately described functions, or even in reverse order in someexamples, depending on the functionality involved, so long as theoverall functionality of the described method is maintained.Furthermore, similar combinations of hardware and/or software elementscan be employed to implement the methods described in connection withother flowcharts provided in the present disclosure, such as theadditional flowchart shown in FIG. 3B.

Initially at block 302, method 300 of FIG. 3A includes receiving aplurality of remission signals based on a portion of a lane of travel inan environment in response to at least one sensor of the vehicle sensingthe portion of the lane of travel. As previously noted, the vehicle maybe the vehicle described in reference to FIGS. 1 and 2 may be configuredto operate in an autonomous mode in the environment. The environment maybe any environment in which the vehicle may be operating autonomously ona surface with a lane of travel. The vehicle may be operating and/orpositioned substantially in the lane of travel.

The surface may include any type of surface or medium that a vehicle maytravel upon. For example, some surfaces may represent a road of sometype including, for example, a highway or an interstate to name a few.In other examples, the surface may represent other mediums of travelincluding water or air. The sensor may be a LIDAR the same as or similarto the LIDAR described with reference to LIDAR Unit 128 of FIG. 1.

The remission signals may include a plurality of laser data points thatare based on objects in the environment of the vehicle, and morespecifically, data points that are based on lane markers in the portionof the lane of travel in the environment. Vehicle components such as acomputing device or processor may be configured to receive the remissionsignals. For example, the vehicle may have a LIDAR unit that illuminatesareas around, surrounding, in front of, behind, to the side, or in anyproximity or relation to the vehicle, and detects reflected light. Insome cases of operation, the LIDAR may rotate (e.g., periodically) andemit laser beams. Other motions may be taken by the LIDAR as well.

An autonomous vehicle may utilize a large number of scans or periodicscans to establish successive scans. Successive scans may be multiplescans by the LIDAR unit occurring over time and may be continuous or mayoccur in intervals. Other examples of successive scans may exist aswell. Reflections from the emitted laser beams by lane markers in thelane of travel of the environment may then be received as remissionsignals by suitable sensors.

Time-stamping receipt of the reflected signals may allow for associatingeach reflected signal (if any is received at all) with the most recentlyemitted laser pulse, and measuring the time delay between emission ofthe laser pulse and reception of the reflected light. The time delay mayprovide an estimate of the distance to the reflective feature by scalingaccording to the speed of light in the intervening atmosphere. Combiningthe distance information for each reflected signal with the orientationof the LIDAR device for the respective pulse emission may allow fordetermining a position of the reflective feature in three-dimensions.For illustrative purposes, an environmental scene can be described inthe two-dimensional x-y plane in connection with a single sweep of theLIDAR device that estimates positions to a series of points located inthe x-y plane. However, it is noted that a more completethree-dimensional sampling is provided by either adjusting beam steeringoptics to direct the laser beam up or down from the x-y plane on itsnext sweep of the scene or by providing additional lasers and associatedbeam steering optics dedicated to sampling point locations in planesabove and below the x-y plane, or combinations of these.

A computing device of a vehicle may be configured to store thesuccessive scans in various types of memory. The computing device maycapture successive scans in intervals or may be executed simultaneously.Other examples of accumulating successive scans may exist as well.Accordingly, as a vehicle continues to operate in a lane of travel,successive scans by a LIDAR unit may provide returns that show thereflectiveness of a detected portion of the lane of travel.

Block 304 includes comparing the plurality of remission signals to aknown remission value indicative of a level of reflectiveness for a lanemarker in (or associated with) the lane of travel. The comparison may bemade to determine whether the remission signals obtained at block 302are indicative of the lane marker. To do so, a computing device of avehicle may determine whether the level of reflectiveness of theplurality of remission signals matches or is equivalent to a generallyknown level of reflectiveness for a lane marker by comparing the tworeflectiveness values. In other words, the vehicle may compare thereflectivity of received remission signals to a threshold reflectivenessthat represents the reflectivity of the lane marker known at that time.

In other examples, the computing device may determine that the remissionsignals obtained at block 302 are indicative of the lane marker level ofreflectiveness based on the level of reflectiveness of the plurality ofremission signals being within a certain range of the generally knownlevel of reflectiveness.

The generally known level of reflectiveness for a lane marker may beknown by the vehicle (e.g., stored in a data storage the same as orsimilar to data storage 114 of a computer system similar to or the sameas computer system 112) prior to the vehicle scanning the lane of travelfor lane markers. For example, the vehicle may maintain a data base(e.g., using data storage 114) that includes statistics on the recentdistribution of LIDAR remission values obtained by the vehicle, as wellas statistics on the level of reflectivity for lane markers that haverecently been reported. In other examples, the known level ofreflectiveness may be received from a server or database external to thevehicle as the vehicle operates in the environment and the comparisonmay be made in real time.

To make the comparison, in some examples, the vehicle may use itscomponents (e.g., a computer system) and determine an average remissionvalue based on respective remission signals of the plurality ofremission signals. In other words, the vehicle may determine an averageremission value based on each remission signal of the plurality ofremission signals that may be indicative of an average level ofreflectiveness for the portion of the lane of travel. Using the averageremission value, the vehicle may compare it to the generally knownremission value indicative of a level of reflectiveness for the lanemarkers on the road upon which the vehicle is traveling. In otherexamples, the vehicle may use its components (e.g., a computer system)and determine a variance based on the average remission signals andrespective remission signals of the plurality of remission signals.Thereafter variance may be used during the comparison to, for example,categorize respective remission signals (e.g., high reflectivity, lowreflectivity, average reflectivity).

At block 306, method 300 includes based on the comparison, determiningwhether the portion of the lane of travel in the environment isindicative of a presence of the lane marker. To make the determination,a computing device may be configured to compare the reflectiveness ofthe portion to a threshold level of reflectiveness indicative of a lanemarker in the manner indicated above at block 304. In examples in whichan average remission value is determined, the vehicle may determine theportion of the lane of travel in the environment is indicative of thepresence of the lane marker based on the average remission value beingequivalent to or within a range of the generally known remission value.Alternatively, the vehicle may determine the portion of the lane oftravel in the environment is not indicative of the presence of the lanemarker based on the average remission value not being equivalent to orbeing outside a range of the generally known remission value.

In some examples, after the determination has been made, the vehicle maybe provided with instructions to control the vehicle in the autonomousmode. For example, once the vehicle has determined the location of oneor more lane markers, instructions may be provided to the vehicle toremain within the lane markers and thereby remain within the lane. Thevehicle may be provided with other similar operating instructions aswell.

Further, in some examples, once the presence of a lane marker has beendetermined the vehicle may be provided instructions to use informationsurrounding the located lane marker to determine the location of otherlane markers. For example, a computing device of the vehicle may beconfigured to utilize the relative position of that lane marker todetermine other lane markers. The computing device may utilizeinformation received from other sensors such as a RADAR or GPS, inaddition to the information provided by the LIDAR unit to determine thepresence of other lane markers that may be present in the lane oftravel.

FIG. 4 is a conceptual side view illustration of example detection of alane marker (or reflective marker) in a lane of travel, in accordancewith at least some embodiments described herein. In FIG. 4, anautonomous vehicle 402 is travelling in environment 400 substantially inlane of travel 404 with a lane marker 408 on the surface in front of thevehicle 402. Autonomous vehicle 402 may include a LIDAR unit 406 thatmay be configured to receive laser data collected for a portion 410 oflane of travel 404 and in front of the vehicle 402. The autonomousvehicle 402 may travel in lane of travel 404 and utilize the LIDAR unit406 to detect lane markers and other objects, such as lane marker 408.Lane marker 408 may, for example, be detected by LIDAR unit 406 whenlaser beams are reflected by the lane marker.

In other examples (not shown in FIG. 4) the autonomous vehicle 402 maybe replaced by other types of vehicles, including vehicle 100 shown inFIG. 1 and vehicle 200 shown in FIG. 2. The autonomous vehicle may becapable of being driven by a driver or by a computing device. Theautonomous vehicle 402 may be traveling towards the lane marker 408 atany speed.

As noted above with regard to block 302 of method 300, surface 404 shownin FIG. 4 may represent any type of surface or medium that a vehicle maytravel upon. In the example, the surface 404 may represent a road ofsome type. In other examples, the surface 404 may represent othermediums of travel, including water or air. For example, a boat may betraveling in water and need to identify reflective markers at a longrange away from the boat. The lane marker 408 shown within the examplemay be a reflective lane marker, a cat eye, or another reflective entitythat may help demarcate lane of travel 404.

The autonomous vehicle 402 shown in the example illustrated by FIG. 4also includes a LIDAR unit 406 attached to the front of the vehicle. Inother implementations, the LIDAR unit may be coupled to other portionsof the vehicle. Further, a vehicle may include additional LIDAR units.The LIDAR unit 406 may be configured to scan for lane markers in anyrange. For example, a vehicle may be configured to include a long rangeLIDAR unit and a short range LIDAR unit that are each calibrated tofocus upon certain ranges in front of the vehicle. The various rangesLIDAR units may be capable of being programmed by a computing device inreal-time.

In the example shown by FIG. 4, the LIDAR unit 406 is actively scanningin front of the vehicle 402 in portion 410 for any lane markers,including detected lane marker 408. As the vehicle 402 travels towardsthe lane marker 408, LIDAR unit 406 may detect the portion of the lane404 in successive scans. In the successive scans, the lane marker 408may initially return a certain remission signal reflectivity level, butthe reflectivity level may change as the vehicle continues to scan theportion. A computing device may utilize the information obtained fromthe successive scans by the LIDAR unit to determine that some of thereflectivity received during the successive scans is evident that theportion of the lane of travel contains a lane marker.

Referring back to FIG. 3B, provided is another method 320 for filteringnoisy or high-intensity regions in laser-based lane marker detection.Similar to method 300 of FIG. 3A, the vehicle described in this methodmay be vehicle 100 and/or vehicle 200 as illustrated and described inreference to FIGS. 1 and 2, respectively, or components of the vehicle100 or vehicle 200. Method 320 may be performed in addition to, at thesame time, or subsequently to method of 300 of FIG. 3A.

Initially at block 322, method 320 includes receiving a second pluralityof remission signals based on a second portion of the lane of travel inthe environment. The second portion of lane of travel may be differentthan the first portion of the lane of travel in the environment, andconsequently, the second plurality of remission signals from the secondportion of the lane of travel in the environment may be different thanthe plurality of remission signals received at block 302 of method 300.The second plurality of remission signals may be received in a mannerthe same as or similar to that described in reference to block 302 ofmethod 300.

At block 324, method 320 includes determining a region of the lane oftravel. The region of the lane of travel may include the first portionof the lane of travel and the second portion of the lane of travel.Regions of the lane of travel may be determined based on transitionssuch as a lane of travel that transitions from a flat road to a hillyroad or a lane of travel that transitions from uncovered road to road ina tunnel or under a bridge, for example. Other transitions are possibleas well. For example, a vehicle may determine based on the angle ofincidence of an operating LIDAR sensor that it is transitioning from aflat road to a road with a hill portion. Regions may be determined inother manners as well. In some examples, the region may be determinedrandomly.

Block 326 includes determining an average remission value based onrespective remission signals of the plurality of remission signals andrespective remission signals of the second plurality of remissionsignals. The average remission value may be determined in a manner thesame as or similar to the average remission value described above inreference to block 304 of method 300.

At block 328, method 320 includes comparing the average remission valueto the known remission value. The average remission value may becompared to the generally known remission value in a manner similar toor the same as that described above in reference to block 304 of method300.

Block 330 includes, based on the average remission value being greaterthan the known remission value, causing the known remission value to bechanged in a manner such that the known remission value is greater thanthe average remission value for the region. For example, if the averageof the remission values returned over a region of the road the vehicleis traveling on begins to dramatically or substantially increase, maybedue to brighter pavement or one of the transitions noted above, theremission threshold necessary to be considered a lane marker may beincreased. In some examples, the threshold may be set based on or as afunction of the average threshold and the variance of the plurality ofremission signals (described above in regard to block 304 of method300). Thereafter, as the vehicle continues to operate in the region,detect and compare lane markers, the vehicle may utilize the updatedknown remission value (i.e., threshold) instead of the previously known(or outdated) remission value to determine the presence of lane markers.

In other examples (not shown in method 320), when the number of detectedlane markers (based on the remission threshold necessary to beconsidered a lane marker) increases above a certain value, it may beindicative that the threshold may be set too low (e.g., immediatelyafter a change in pavement type from darker to brighter). In thissituation, the remission threshold necessary to be considered a lanemarker may be temporarily increased even higher in order to limit falsepositive detections.

FIG. 5 is a conceptual illustration of another example detection of alane marker (or reflective marker) in a lane of travel. FIG. 5illustrates an aerial view of a vehicle that is detecting lane markersat various distances away from the vehicle and in different portions ofa lane of travel. Within the example conceptual illustration, FIG. 5includes a vehicle 500 with a LIDAR unit 502 for detecting lane markerssuch as lane markers 504 and 506.

Similar to FIG. 4, the vehicle 500 may be replaced by any type ofvehicle, including vehicle 100 illustrated in FIG. 1 and vehicle 200illustrated in FIG. 2. The vehicle 500 may operate autonomously or maybe controlled by a driver. Vehicle 500 may include a LIDAR unit 502 thatoperates to receive laser data from various portions of lane of travel512 of which vehicle 500 is operating and substantially positioned inthe lane of travel. The LIDAR unit 502 may be placed at different pointsof the vehicle 500. In the example, the LIDAR unit 502 is positioned onthe top of the vehicle 500. Furthermore, the vehicle 500 may includeadditional LIDAR units. The lane markers 504 and 506 shown in theexample represent lane markers or cat eyes. In other examples, the lanemarkers 504 and 506 may represent similar reflective materials thatindicate position on a road.

In the example, the vehicle 500 may be traveling along the road andutilizing sensors, such as LIDAR unit 502 to identify lane markers thatmay define the path which vehicle 500 is traveling along. To do so,vehicle 500 may scan portion 508 of lane of travel 514 using LIDAR unit502. A computing device associated with the vehicle 500 may determinethat the objects in the center of portion 508 are a lane marker 506.Using the methods described above, the computing device may receiveLIDAR scans to determine that the entities are lane markers.

As vehicle 500 continues to operate in lane of travel 514, vehicle 500may scan different portions 510 and 512 of lane of travel 514. Based onthe LIDAR scans received by the computing device of vehicle 500, vehicle500 may determine that the pavement of lane of travel 514 has becomebrighter in those areas (indicated in FIG. 5 by the gray area). In theexample, portions 510 and 512 may be included in region 516.Accordingly, to recognize lane markers, in region 516, vehicle 500 mayupdate or change its threshold level of reflectiveness for a lanemarker. Thereafter, as vehicle 500 operates in region 516, vehicle 500may use the updated (in this example higher) threshold when determiningthe presence of lane markers.

Example methods, such as method 300 of FIG. 3A or method 320 of FIG. 3Bmay be carried out in whole or in part by the vehicle and itssubsystems. Accordingly, example methods could be described by way ofexample herein as being implemented by the vehicle. However, it shouldbe understood that an example method may be implemented in whole or inpart by other computing devices. For example, an example method may beimplemented in whole or in part by a server system, which receives datafrom a device such as those associated with the vehicle. Other examplesof computing devices or combinations of computing devices that canimplement an example method are possible.

In some embodiments, the techniques disclosed herein may be implementedas computer program instructions encoded on a non-transitorycomputer-readable storage media in a machine-readable format, or onother non-transitory media or articles of manufacture (e.g., theinstructions 115 stored on the data storage 114 of the computer system112 of the vehicle 100). FIG. 6 is a schematic illustrating a conceptualpartial view of an example computer program product that includes acomputer program for executing a computer process on a computing device,arranged according to at least some embodiments presented herein.

In one embodiment, the example computer program product 600 is providedusing a signal bearing medium 602. The signal bearing medium 602 mayinclude one or more programming instructions 604 that, when executed byone or more processors may provide functionality or portions of thefunctionality described herein. In some examples, the signal bearingmedium 602 can be a non-transitory computer-readable medium 606, suchas, but not limited to, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, memory, etc. In someimplementations, the signal bearing medium 602 may encompass a computerrecordable medium 608, such as, but not limited to, memory, read/write(R/W) CDs, R/W DVDs, etc. In some implementations, the signal bearingmedium 602 may encompass a communications medium 610, such as, but notlimited to, a digital and/or an analog communication medium (e.g., afiber optic cable, a waveguide, a wired communications link, a wirelesscommunication link, etc.). Thus, for example, the signal bearing medium602 may be conveyed by a wireless form of the communications medium 610.

The one or more programming instructions 604 may be, for example,computer executable and/or logic implemented instructions. In someexamples, a computing device such as the computer system 112 of FIG. 1may be configured to provide various operations, functions, or actionsin response to the programming instructions 604 conveyed to the computersystem 112 by one or more of the computer readable medium 606, thecomputer recordable medium 608, and/or the communications medium 610.

The non-transitory computer readable medium could also be distributedamong multiple data storage elements, which could be remotely locatedfrom each other. The computing device that executes some or all of thestored instructions could be a vehicle, such as the example vehicle 200illustrated in FIG. 2. Alternatively, the computing device that executessome or all of the stored instructions could be another computingdevice, such as a server.

The above detailed description describes various features and functionsof the disclosed systems, devices, and methods with reference to theaccompanying figures. While various aspects and embodiments have beendisclosed herein, other aspects and embodiments are possible. Thevarious aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

We claim:
 1. A method comprising: receiving, by one or more processors,from at least one sensor of an autonomous vehicle, a plurality ofremission signals based on a portion of a lane of travel in anenvironment, wherein a given remission signal of the plurality ofremission signals comprises a respective remission value indicative of alevel of reflectiveness for the portion of the lane of travel, whereinthe autonomous vehicle is substantially in the lane of travel in theenvironment; determining, by the one or more processors, an averageremission value based on respective remission signals of the pluralityof remission signals, wherein the average remission value is indicativeof an average level of reflectiveness for the portion of the lane oftravel; comparing, by the one or more processors, the average remissionvalue to a known remission threshold indicative of a minimum level ofreflectiveness for a lane marker in the lane of travel; based on thecomparison, the one or more processors adjusting the known remissionthreshold to be an adjusted known remission threshold, wherein theadjusted remission threshold is used for only a predetermined period oftime as the autonomous vehicle navigates in the lane of travel; andresponsive to the adjusting, the one or more processors (i) determining,based on the adjusted known remission threshold, that the portion of thelane of travel includes at least one lane marker demarcating the lane oftravel, and (ii) controlling the autonomous vehicle to navigate the laneof travel within the determined at least one lane marker demarcating thelane of travel.
 2. The method of claim 1, wherein determining that theportion of the lane of travel includes the at least one lane markerdemarcating the lane of travel based on the adjusted known remissionthreshold comprises: comparing the plurality of remission signals to theadjusted known remission threshold; and based on the comparison of theplurality of remission signals to the adjusted known remissionthreshold, determining that the portion of the lane of travel includesat least one lane marker demarcating the lane of travel.
 3. The methodof claim 1, wherein adjusting the known remission threshold to be anadjusted known remission threshold based on the comparison comprises:based on the comparison indicating that the average remission value isgreater than the known remission threshold, the one or more processorsadjusting the known remission threshold to be higher such that theadjusted known remission threshold is greater than the average remissionvalue.
 4. The method of claim 1, wherein adjusting the known remissionthreshold to be an adjusted known remission threshold based on thecomparison comprises: based on the comparison indicating that theaverage remission value is less than the known remission threshold, theone or more processors adjusting the known remission threshold to belower such that the adjusted known remission threshold is less than theaverage remission value.
 5. The method of claim 1, wherein thepredetermined period of time is a period of time during which at least aportion of the autonomous vehicle travels past the portion of the laneof travel.
 6. The method of claim 1, wherein determining that theportion of the lane of travel includes the at least one lane markerdemarcating the lane of travel comprises determining that the portion ofthe lane of travel includes at least one lane marker on one side of thelane of travel and further includes at least one other lane marker on anopposite side of the lane of travel, and wherein controlling theautonomous vehicle to navigate the lane of travel within the determinedat least one lane marker demarcating the lane of travel comprisescontrolling the autonomous vehicle to navigate between the at least onelane marker and the at least one other lane marker.
 7. The method ofclaim 1, wherein the portion of the lane of travel is a first portion ofthe lane of travel, the method further comprising: receiving, by the oneor more processors, a second plurality of remission signals based on asecond portion of the lane of travel in the environment different fromthe first portion, wherein a given remission signal of the secondplurality of remission signals comprises a respective second remissionvalue indicative of a level of reflectiveness for the second portion ofthe lane of travel; and determining, by the one or more processors, aregion of the lane of travel, wherein the region comprises the firstportion and the second portion, wherein determining the averageremission value is further based on respective remission signals of thesecond plurality of remission signals, wherein the average remissionvalue is further indicative of an average level of reflectiveness forthe region of the lane of travel, and wherein the known remissionthreshold is further indicative of a minimum level of reflectiveness fora lane marker in the region of the lane of travel.
 8. The method ofclaim 7, further comprising: responsive to the adjusting, the one ormore processors (i) determining, based on the adjusted known remissionthreshold, that the region of the lane of travel includes at least onelane marker demarcating the lane of travel, and (ii) controlling theautonomous vehicle to navigate in the region of the lane of travelwithin the determined at least one lane marker demarcating the lane oftravel.
 9. The method of claim 1, further comprising: based on theplurality of remission signals and further based on the known remissionthreshold, determining that the portion of the lane of travel in theenvironment includes a particular number of lane markers; comparing theparticular number of lane markers to a particular threshold number oflane markers; and based on the comparison indicating that the particularnumber of lane markers exceeds the particular threshold, making adetermination that the particular number of lane markers includes atleast one false positive lane marker, wherein adjusting the knownremission threshold to be the adjusted known remission threshold isfurther based on the determination that the particular number of lanemarkers includes at least one false positive lane marker.
 10. Anautonomous vehicle comprising: at least one sensor configured to sense alane of travel in an environment of the autonomous vehicle, wherein theautonomous vehicle is substantially in the lane of travel in theenvironment; a computer system, wherein the computer system isconfigured to: receive, from the at least one sensor, a plurality ofremission signals based on a portion of the lane of travel in theenvironment, wherein a given remission signal of the plurality ofremission signals comprises a respective remission value indicative of alevel of reflectiveness for the portion of the lane of travel; determinean average remission value based on respective remission signals of theplurality of remission signals, wherein the average remission value isindicative of an average level of reflectiveness for the portion of thelane of travel; compare the average remission value to a known remissionthreshold indicative of a minimum level of reflectiveness for a lanemarker in the lane of travel; based on the comparison, adjust the knownremission threshold to be an adjusted known remission threshold, whereinthe adjusted remission threshold is used for only a predetermined periodof time as the autonomous vehicle navigates in the lane of travel; andresponsive to the adjusting, (i) determine, based on the adjusted knownremission threshold, that the portion of the lane of travel includes atleast one lane marker demarcating the lane of travel, and (ii) controlthe autonomous vehicle to navigate the lane of travel within thedetermined at least one lane marker demarcating the lane of travel. 11.The autonomous vehicle of claim 10, wherein the at least one sensorcomprises a laser detection and ranging system.
 12. The autonomousvehicle of claim 10, wherein the portion of the lane of travel is afirst portion of the lane of travel, and wherein the computer system isfurther configured to: receive a second plurality of remission signalsbased on a second portion of the lane of travel in the environmentdifferent from the first portion, wherein a given remission signal ofthe second plurality of remission signals comprises a respective secondremission value indicative of a second level of reflectiveness for thesecond portion of the lane of travel; determine a region of the lane oftravel, wherein the region comprises the first portion and the secondportion; determine a second average remission value based on respectiveremission signals of the second plurality of remission signals, whereinthe second average remission value is indicative of an average level ofreflectiveness for the second portion of the lane of travel; compare theaverage remission value to the known remission threshold; based on thecomparison, determine a second adjusted known remission thresholddifferent from the adjusted known remission threshold, wherein theadjusted known remission threshold is associated with the first portionof the lane of travel, and wherein the second adjusted known remissionthreshold is associated with the second portion of the lane of travel;and responsive to determining the second adjusted known remissionthreshold, (i) determine, based on the second adjusted known remissionthreshold, that the second portion of the lane of travel includes atleast one second lane marker demarcating the lane of travel, and (ii)control the autonomous vehicle to navigate the region of the lane oftravel within the determined at least one second lane marker demarcatingthe lane of travel substantially simultaneously as the controlling ofthe autonomous vehicle to navigate the lane of travel within thedetermined at least one lane marker.
 13. The autonomous vehicle of claim10, wherein the predetermined period of time is a period of time duringwhich at least a portion of the autonomous vehicle travels past theportion of the lane of travel.
 14. The autonomous vehicle of claim 10,wherein determining that the portion of the lane of travel includes theat least one lane marker demarcating the lane of travel comprisesdetermining that the portion of the lane of travel includes at least onelane marker on one side of the lane of travel and further includes atleast one other lane marker on an opposite side of the lane of travel,and wherein controlling the autonomous vehicle to navigate the lane oftravel within the determined at least one lane marker demarcating thelane of travel comprises controlling the autonomous vehicle to navigatebetween the at least one lane marker and the at least one other lanemarker.
 15. A non-transitory computer readable medium having storedtherein instructions that, when executed by a computer system of anautonomous vehicle, cause the computer system to perform operationscomprising: receiving, from at least one sensor of the autonomousvehicle, a plurality of remission signals based on a portion of a laneof travel in an environment, wherein a given remission signal of theplurality of remission signals comprises a respective remission valueindicative of a level of reflectiveness for the portion of the lane oftravel, wherein the autonomous vehicle is substantially in the lane oftravel in the environment; determining an average remission value basedon respective remission signals of the plurality of remission signals,wherein the average remission value is indicative of an average level ofreflectiveness for the portion of the lane of travel; comparing theaverage remission value to a known remission threshold indicative of aminimum level of reflectiveness for a lane marker in the lane of travel;based on the comparison, adjusting the known remission threshold to bean adjusted known remission threshold, wherein the adjusted remissionthreshold is used for only a predetermined period of time as theautonomous vehicle navigates in the lane of travel; and responsive tothe adjusting, (i) determining, based on the adjusted known remissionthreshold, that the portion of the lane of travel includes at least onelane marker demarcating the lane of travel, and (ii) controlling theautonomous vehicle to navigate the lane of travel within the determinedat least one lane marker demarcating the lane of travel.
 16. Thenon-transitory computer readable medium of claim 15, wherein determiningthat the portion of the lane of travel includes the at least one lanemarker demarcating the lane of travel based on the adjusted knownremission threshold comprises: comparing the plurality of remissionsignals to the adjusted known remission threshold; and based on thecomparison of the plurality of remission signals to the adjusted knownremission threshold, determining that the portion of the lane of travelincludes at least one lane marker demarcating the lane of travel. 17.The non-transitory computer readable medium of claim 15, whereinadjusting the known remission threshold to be an adjusted knownremission threshold based on the comparison comprises: based on thecomparison indicating that the average remission value is greater thanthe known remission threshold, the one or more processors adjusting theknown remission threshold to be higher such that the adjusted knownremission threshold is greater than the average remission value.
 18. Thenon-transitory computer readable medium of claim 15, wherein adjustingthe known remission threshold to be an adjusted known remissionthreshold based on the comparison comprises: based on the comparisonindicating that the average remission value is less than the knownremission threshold, the one or more processors adjusting the knownremission threshold to be lower such that the adjusted known remissionthreshold is less than the average remission value.
 19. Thenon-transitory computer readable medium of claim 15, wherein thepredetermined period of time is a period of time during which at least aportion of the autonomous vehicle travels past the portion of the laneof travel.
 20. The non-transitory computer readable medium of claim 15,the operations further comprising: based on the plurality of remissionsignals and further based on the known remission threshold, determiningthat the portion of the lane of travel in the environment includes aparticular number of lane markers; comparing the particular number oflane markers to a particular threshold number of lane markers; and basedon the comparison indicating that the particular number of lane markersexceeds the particular threshold, making a determination that theparticular number of lane markers includes at least one false positivelane marker, wherein adjusting the known remission threshold to be theadjusted known remission threshold is further based on the determinationthat the particular number of lane markers includes at least one falsepositive lane marker.